Selecting an AI Marketing Agency: RFP Template, Scorecard, and Red Flags

If you need to select an AI marketing agency, this guide gives you everything required to run a clean, defensible process. You will find a ready-to-copy RFP for AI marketing, an objective evaluation scorecard, and the biggest red flags to watch for when you hire an AI marketing agency.

For related guidance, see in-house vs agency content automation, implement AI in digital marketing, and agency marketing services.

The fast path to AI marketing agency selection

• Clarify outcomes before tools. Define the business problems and the measurable results you expect.
• Shortlist 3 to 5 agencies that match your industry, channels, and data complexity.
• Issue a tight RFP with requirements and scoring criteria upfront.
• Run structured demos against your use cases with the same data brief.
• Score independently, debrief as a team, and document the decision.

What to define before you invite agencies

1. Business objectives - revenue, pipeline, CAC, LTV, retention, media efficiency, creative throughput.
2. Primary use cases - predictive segmentation, media mix modeling, lead scoring, content generation, SEO at scale, product recommendations, lifecycle automation.
3. Data and tools - CRMs, CDPs, analytics, MAPs, ad platforms, CMS, data warehouses, model hosting, experimentation stack.
4. Constraints - compliance, security, brand guardrails, geography, languages, resource availability.
5. Governance - who approves, who signs, reporting cadence, acceptable use policy for generative AI.

Copy-ready RFP template for AI marketing

Use the outline below as your RFP for AI marketing. Replace bracketed prompts with your details. Share the same brief with all vendors to keep the process fair.

Title: Request for Proposal - AI Marketing Services for [Company Name]

1. Background and Objectives
- About us: [Industry, markets, audience]
- Current state: [Channels, stack, team size]
- Objectives: [Revenue, pipeline, CAC, retention, efficiency]
- Success metrics: [Primary KPI, secondary KPIs, target lift or timeframe]

2. Scope of Services
- Use cases: [e.g., predictive lead scoring, content generation, MMM]
- Channels: [Paid search, paid social, SEO, email, web, retail media]
- Deliverables: [Models, playbooks, content assets, experiments]
- Collaboration model: [Pilot, retainer, embedded team, training]

3. Data and Technology Environment
- Data sources and access: [CRM, CDP, DWH, analytics]
- Data quality notes: [Gaps, latency, PII handling]
- Tooling constraints: [Approved vendors, on-prem vs cloud]
- Integration needs: [APIs, connectors, ETL, tag management]

4. Approach and Methodology
- Discovery and hypothesis process
- Model selection and evaluation
- Human-in-the-loop QA and compliance guardrails
- Experiment design and measurement standards

5. Team and Experience
- Roles and seniority mix
- Relevant case studies with metrics and methods
- Certifications and partnerships

6. Security, Compliance, and Ethics
- Data protection, encryption, residency
- DPA readiness and third-party tool vetting
- Content safety, bias mitigation, model governance

7. Work Plan and Timeline
- Milestones: [Kickoff, discovery, pilot, go-live]
- Dependencies and stakeholder time required

8. Pricing and Commercials
- Rate card and blended rates
- Fixed-fee pilot proposal with success criteria
- Licensing or usage-based costs
- Assumptions, exclusions, and change control

9. SLAs, Reporting, and Governance
- SLAs: [Turnaround, uptime if applicable]
- Reporting cadence and dashboards
- Executive reviews and escalation path

10. References and Validations
- 2-3 relevant client references
- Example artifacts: dashboards, prompts, templates

11. Evaluation Criteria and Scoring
- We will use the attached weighted scorecard (see below)

12. Submission Instructions
- Timeline: [Questions due, proposals due, demo dates]
- Format: [Page limit, file format]
- Contacts: [Primary contact, procurement]

Weighted evaluation scorecard

Score each agency from 1 to 5 per criterion, multiply by the weight, then sum for the total score. Keep individual scores private until the group debrief to avoid anchoring.

Criterion	Description	Weight	Score 1-5	Weighted
Impact on business goals	Clarity on how the plan drives revenue, CAC, LTV, or efficiency	15%
AI capabilities and methodology	Modeling rigor, prompt engineering, evaluation, and governance	15%
Data and integration readiness	Plan to ingest, clean, and connect to your stack	10%
Channel and domain expertise	Proof of performance in your channels and industry	8%
Measurement and experimentation	Lift measurement, MMM, incrementality, test design	8%
Team quality and seniority	Hands-on experts, not only advisors	12%
Security and compliance	Policies, tooling approvals, and control of third parties	7%
Case studies and proof	Baseline, counterfactuals, and clear methods	8%
Cultural and process fit	Ways of working, transparency, and communication	5%
Price and value	ROI potential, transparency, and risk sharing	12%
Total		100%

Spreadsheet-ready scorecard template

Criteria,Weight,Score (1-5),Weighted Score,Notes
Impact on business goals,0.15,,,
AI capabilities and methodology,0.15,,,
Data and integration readiness,0.10,,,
Channel and domain expertise,0.08,,,
Measurement and experimentation,0.08,,,
Team quality and seniority,0.12,,,
Security and compliance,0.07,,,
Case studies and proof,0.08,,,
Cultural and process fit,0.05,,,
Price and value,0.12,,,
TOTAL,,=SUM(D2:D11),,

Scoring guidance:

• 5 - Exemplary and validated with evidence and metrics
• 4 - Strong with minor gaps
• 3 - Adequate but requires support
• 2 - Material weaknesses or unproven
• 1 - Does not meet requirements

12 interview questions to separate signal from noise

1. Walk us through a recent engagement where you improved a core business metric. How did you measure lift and confidence?
2. What models or techniques do you use for our top use case, and how do you evaluate them?
3. Show the workflow from data ingestion to insight to activation. Who owns each step?
4. How do you prevent and detect hallucinations in generative outputs?
5. What are your standards for experiment design and stopping rules?
6. Describe your prompt libraries and content QA process, including brand and legal guardrails.
7. What happens if our data quality is lower than expected? What is your remediation plan?
8. How do you ensure reproducibility and documentation of your work?
9. What will be in our control versus yours, and what is the exit plan?
10. Describe a time your approach did not work. What changed after the postmortem?
11. Which third-party tools do you rely on, and how are they vetted for security and compliance?
12. How do you structure pilots so that success or failure is unambiguous?

Red flags when you hire an AI marketing agency

• No transparency - vague claims, no methodology, or unwillingness to explain models and measurement.
• Vanity metrics - focuses on clicks or impressions without tying to revenue, CAC, or LTV.
• Secret sauce dependency - proprietary black boxes with unclear IP or data rights that create lock-in.
• No human in the loop - fully automated content or decisioning with no quality control.
• Weak security posture - cannot provide a DPA, lacks SOC 2 or ISO controls, or ignores PII handling.
• Refuses a pilot - will not run a time-bound, fixed-fee proof of value with clear success criteria.
• Overpromising - guaranteed lifts or unrealistic timelines with no plan for data readiness.
• Poor references - cannot provide relevant references or shows case studies without baselines.
• One-trick tool - pitches a single model or platform for every problem regardless of fit.
• No experiment discipline - lacks holdouts, pre-post, or MMM where applicable.

Example 6-week selection timeline

1. Week 1 - finalize objectives, data inventory, and RFP draft.
2. Week 2 - issue RFP to shortlist, host one Q&A call, confirm demo use cases.
3. Week 3 - receive proposals, run security questionnaires in parallel.
4. Week 4 - vendor demos against the same brief, collect independent scores.
5. Week 5 - team debrief, reference checks, negotiate pilot scope and price.
6. Week 6 - finalize MSA, DPA, SOW, and kick off the pilot.

Pilot structure and SOW checkpoints

• Problem statement - the metric to move, baseline, and target lift.
• Data plan - sources, access, privacy controls, and transformation steps.
• Operating model - roles, meeting cadence, and decision rights.
• Experiment design - hypotheses, cohorts, timeline, and stopping rules.
• Success criteria - what constitutes a pass, including stats or confidence.
• Handover and exit - what is documented, what is yours to keep, and knowledge transfer.

Putting it all together

To select an AI marketing agency with confidence, anchor on business outcomes, issue a clear RFP, and apply an objective scorecard. Use the templates above to accelerate your AI marketing agency selection and run a fair evaluation. When you are ready to hire an AI marketing agency, start with a fixed-fee pilot and let results guide the long-term partnership.

Pro tip: Share the same data brief and demo script with every vendor. This makes comparisons fair and the final decision easier to defend.

FAQ

What should an AI marketing agency RFP require?
Ask for workflow diagrams, governance policies, sample prompts, measurement plans, data handling practices, and references in your industry. Require proof of human QA, not just tool lists.

What are red flags when hiring an AI marketing agency?
Avoid vendors promising fully autonomous publishing, vague IP ownership, no audit trails, or inability to explain model selection and fallback processes.

How do you score AI agency proposals fairly?
Weight strategic fit, integration depth, compliance readiness, and pilot design over demo polish. Use the same scorecard for in-house build vs agency options when possible.